#11-add pylint action

.github/workflows/ci.yml

@@ -0,0 +1,30 @@
+name: CI-Pipeline
+
+on:
+  pull_request:
+    branches: [ main ]
+  workflow_dispatch:
+
+env:
+  PYTHON_VERSION: 3.11.5
+
+jobs:
+  pylint:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v4
+
+      - name: Set up python ${{ env.PYTHON_VERSION }}
+        uses: actions/setup-python@v5
+        with:
+          python-version: ${{ env.PYTHON_VERSION }}
+
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          pip install pylint
+
+      - name: Analyse code with pylint
+        run: |
+          pylint $(git ls-files '*.py') --rcfile=.pylintrc

.pylintrc

@@ -0,0 +1,22 @@
+[tool.pylint.main]
+ignore-patterns = ["^\\.#"]
+
+py-version = 3.11
+
+[tool.pylint.basic]
+no-docstring-rgx = "^_"
+
+[tool.pylint."messages control"]
+disable = raw-checker-failed, bad-inline-option, locally-disabled, file-ignored, suppressed-message, useless-suppression, deprecated-pragma, use-symbolic-message-instead, use-implicit-booleaness-not-comparison-to-string, use-implicit-booleaness-not-comparison-to-zero, missing-module-docstring, wrong-import-order, missing-function-docstring, missing-class-docstring, import-error, cyclic-import
+
+[tool.pylint.variables]
+ignored-argument-names = "_.*|^ignored_|^unused_"
+
+[FORMAT]
+max-line-length=120
+
+[DESIGN]
+max-args=10
+min-public-methods=1
+max-locals=20
+max-attributes=20

data/__init__.py

@@ -1 +1 @@
-from data.dataloader import get_mnist_loader, get_cifar10_loader
+from data.dataloader import get_mnist_loader, get_cifar10_loader

data/dataloader.py

@@ -32,9 +32,8 @@ def get_mnist_loader(
         val_loader = DataLoader(dataset=val_dataset, batch_size=batch_size, shuffle=shuffle, num_workers=num_workers)
         return train_loader, val_loader
 
-    else:
-        test_dataset = MNIST(data_path, train=False, transform=transform, download=True)
-        return DataLoader(dataset=test_dataset, batch_size=batch_size, shuffle=shuffle, num_workers=num_workers)
+    test_dataset = MNIST(data_path, train=False, transform=transform, download=True)
+    return DataLoader(dataset=test_dataset, batch_size=batch_size, shuffle=shuffle, num_workers=num_workers)
 
 
 def get_cifar10_loader(
@@ -64,7 +63,5 @@ def get_cifar10_loader(
         val_loader = DataLoader(dataset=val_dataset, batch_size=batch_size, shuffle=shuffle, num_workers=num_workers)
         return train_loader, val_loader
 
-    else:
-        test_dataset = CIFAR10(data_path, train=False, transform=transform, download=True)
-        return DataLoader(dataset=test_dataset, batch_size=batch_size, shuffle=shuffle, num_workers=num_workers
-                          )
+    test_dataset = CIFAR10(data_path, train=False, transform=transform, download=True)
+    return DataLoader(dataset=test_dataset, batch_size=batch_size, shuffle=shuffle, num_workers=num_workers)

model/scheduler.py

@@ -24,7 +24,8 @@ def forward(self, image: torch.Tensor, noise: torch.Tensor, t: int) -> torch.Ten
 
         return image * batched_sqrt_alpha_cum_prod + noise * batched_sqrt_one_minus_alpha_cum_prod
 
-    def backward_sample(self, noise_image: torch.Tensor, noise_pred: torch.Tensor, t: int) -> Tuple[torch.Tensor, torch.Tensor]:
+    def backward_sample(self, noise_image: torch.Tensor, noise_pred: torch.Tensor, t: int)\
+            -> Tuple[torch.Tensor, torch.Tensor]:
         image = (noise_image - (self.sqrt_one_minus_alpha_cum_prod[t] * noise_pred)) / self.sqrt_alpha_cum_prod[t]
         image = image.clamp(-1, 1)
 
@@ -33,8 +34,8 @@ def backward_sample(self, noise_image: torch.Tensor, noise_pred: torch.Tensor, t
 
         if t == 0:
             return image, mean
-        else:
-            variance = ((1 - self.alpha_cum_prod[t-1]) / (1 - self.alpha_cum_prod[t])) * self.betas[t]
-            sigma = torch.sqrt(variance)
-            z = torch.randn(image.shape).to(self.device)
-            return image, mean + sigma * z
+
+        variance = ((1 - self.alpha_cum_prod[t - 1]) / (1 - self.alpha_cum_prod[t])) * self.betas[t]
+        sigma = torch.sqrt(variance)
+        z = torch.randn(image.shape).to(self.device)
+        return image, mean + sigma * z

utils/__init__.py

@@ -1 +1 @@
-from utils.model import repeat_layers
+from utils.model import repeat_layers